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The performance of version 0.6.3 is weaker than that of version 0.6.2 in stress testing.
Scenario: Agent
Stress Testing Data: Input 50 tokens, output 20 tokens.
Version 0.6.3: 22 QPS, with a significant drop after reaching the maximum value.
Version 0.6.2: 24 QPS, with available performance reaching over 90% at 36 QPS. The decline after reaching the maximum value is gradual.
Under the same conditions, comparing version 0.6.3 with 0.6.2, it was found that the prefill time for version 0.6.3 is about 13ms longer per instance than version 0.6.2. The main reason is a pause of 200-300 microseconds between two blocks.
Conditions for obtaining the data: Using batch_size=20, offline use of LLMEngine with 50 iterations to get the data.
Your current environment (if you think it is necessary)
The output of `python collect_env.py`
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Proposal to improve performance
The performance of version 0.6.3 is weaker than that of version 0.6.2 in stress testing.
Scenario: Agent
Stress Testing Data: Input 50 tokens, output 20 tokens.
Version 0.6.3: 22 QPS, with a significant drop after reaching the maximum value.
Version 0.6.2: 24 QPS, with available performance reaching over 90% at 36 QPS. The decline after reaching the maximum value is gradual.
Under the same conditions, comparing version 0.6.3 with 0.6.2, it was found that the prefill time for version 0.6.3 is about 13ms longer per instance than version 0.6.2. The main reason is a pause of 200-300 microseconds between two blocks.
Conditions for obtaining the data: Using batch_size=20, offline use of LLMEngine with 50 iterations to get the data.
https://github.com/skylee-01/experimental_data/blob/main/nsys_vllm_062.nsys-rep
https://github.com/skylee-01/experimental_data/blob/main/nsys_vllm_063.nsys-rep
Report of performance regression
No response
Misc discussion on performance
No response
Your current environment (if you think it is necessary)
Before submitting a new issue...
The text was updated successfully, but these errors were encountered: